Applying generalised feedforward neural networks to classifying industrial jobs in terms of risk of low back disorders

Asensio-Cuesta, Sabina; Diego-Más, José Antonio; Alcaide-Marzal, Jorge

doi:10.1016/j.ergon.2010.04.007

Cited by 21 publications

(8 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In BP, the network error for the given inputs is calculated, and the weights of the connections between the neurons in the last hidden layer and the output layer are modified according to the extent to which these connections have contributed to form the current error [59].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Artificial intelligence approach to classify unipolar and bipolar depressive disorders

Ergüzel

Sayar

Tarhan

2015

Neural Comput & Applic

View full text Add to dashboard Cite

Machine learning approaches for medical decision-making processes are valuable when both high classification accuracy and less feature requirements are satisfied. Artificial neural networks (ANNs) successfully meet the first goal with its adaptive engine, while natureinspired algorithms are focusing on the feature selection (FS) process in order to eliminate less informative and less discriminant features. Besides engineering applications of ANN and FS algorithms, medical informatics is another emerging field using similar methods for medical data processing. Classification of psychiatric disorders is one of the major focus of medical informatics using artificial intelligence approaches. Being one of the most debilitating psychiatric diseases, bipolar disorder (BD) is frequently misdiagnosed as unipolar disorder (UD), leading to suboptimal treatment and poor outcomes. Thus, discriminating UD and BD at earlier stages of illness could therefore help to facilitate efficient and specific treatment. The use of quantitative electroencephalography (EEG) cordance as a biomarker has greatly enhanced the clinical utility of EEG in psychiatric and neurological subjects. In this context, the paper puts forward a study using two-step hybridized methodology: particle swarm optimization (PSO) algorithm for FS process and ANN for training process. The noteworthy performance of ANN-PSO approach stated that it is possible to discriminate 31 bipolar and 58 unipolar subjects using selected features from alpha and theta frequency bands with 89.89 % overall classification accuracy.

show abstract

Section: Artificial Neural Networkmentioning

confidence: 99%

Artificial intelligence approach to classify unipolar and bipolar depressive disorders

Ergüzel

Sayar

Tarhan

2015

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…Many studies have used ANN modeling in industrial settings to analyze occupational incidents and injuries. The work of Asensio-Cuesta et al [42] and Zurada et al [43] analyzed the effect of specific industrial lifting jobs, and workplace design on lower back pain. The work of Darvishi et al [44] estimated the probability of lower back pain from occupational incidents in industrial units with a prediction accuracy rate of 92% in train and test data, based on sixteen risk factors of lower back pain in such injuries.…”

Section: Artificial Neural Network In Occupational Safetymentioning

confidence: 99%

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

2019

View full text Add to dashboard Cite

The grain handling industry plays a significant role in U.S. agriculture by storing, distributing, and processing a variety of agricultural commodities. Commercial grain elevators are hazardous agro-manufacturing work environments where workers are prone to severe injuries, due to the nature of the activities and workplace. Safety incidents in agro-manufacturing operations generally arise from a combination of factors, rather than a single cause, therefore, research on occupational incidents must look deeper into identifying the underlying causes, through the application of advanced analyses methods. In occupational safety, it is possible to estimate and predict probability of safety risks through developing artificial neural network predictive models. Due to the significance of safety risk assessment in the design and prioritization of effective prevention measures, this study aimed at classifying and predicting causes of occupational incidents in grain elevator agro-manufacturing operations in the Midwest region of the United States. Workers’ compensation claims data, from 2008 to 2016, were utilized for training multilayer perceptron (MLP) and radial basis function (RBF) neural networks. Both MLP and RBF models could predict the probability of safety risks with a high overall accuracy of 60%, 61%. Based on values of AUC (area under the curve) from the ROC (receiving operating charts), both models predicted the probability of individual safety risks with a high accuracy rate of between 71.5% and 99.2%. In addition, sensitivity analysis showed that nature of injury is the most significant determinant of safety risks probability, along with type of injury. The novelty of this study is the use of the artificial neural network methodology to analyze multi-level causes of occupational incidents as the sources of safety risks in bulk storage facilities. The results confirm that artificial neural networks are useful in safety risk estimation, and identifying the incidents’ risk factors. The implementation of safety measures in grain elevators can help in preventing occupational injuries, saving lives, and reducing the occurrence and severity of such incidents in industrial work environments.

show abstract

“…Further, the developed diagnostic system can successfully classify jobs into the low and high risk categories of LBDs based on lifting task characteristics. Asensio-Cuesta et al (2010) have proposed an ANN approach for classifying the risk of LBD presented by certain lifting jobs. McCauley-Bell et al (1999) have developed a predictive model using fuzzy set theory to identify the risk of sustaining occupational injuries and illnesses in today's workplace.…”

Section: Jm2 72mentioning

confidence: 99%

Assessment of occupational health practices in Indian industries

Beriha

Patnaik

Mahapatra

2012

Journal of Modelling in Management

View full text Add to dashboard Cite

Purpose -The purpose of this paper is to assess the perceptions of safety officers on Occupational Health and Safety (OHS) norms extended to the workforce in Indian industries, understand implementation levels and find out deficiencies existing therein. Design/methodology/approach -In this study, the authors utilized factor analysis to develop an instrument specifically responsible for assessing OHS norms in three major industrial sectors through a broad-based questionnaire survey. The pattern of influence of input parameters on outputs such as injury level and material damage is difficult to establish, possibly due to existence of some nonlinear relationship among them. Therefore, a neural network approach is adopted to carry out sensitivity analysis and identify important deficient items. Findings -Exploratory factor analysis has been carried out on the responses to the designed questionnaire. In total, nine factors with 23 items have been extracted and interpreted. As neural networks are capable of mimicking human cognitive process, the perceptions mechanism of safety officers can be easily modeled via neural networks. Sector-wise deficient items have been identified and strategies for their improvement have been proposed.Research limitations/implications -The major limitation may be the number of industrial sectors considered in the study. Although the proposed model is quite generic, its performance needs to be tested with other categories of industries. Practical implications -Although perceptions of safety officers on their immediate work environment help to formulate constructive safety policy and procedures, involvement of a few representatives from the workforce during the implementation level may assist to substantially reduce injury level and material damage, since the workers are more conversant with work practices, are exposed to risk environments and can sustain injuries if accidents occur. Originality/value -The paper uses advanced statistical and intelligent techniques for assessment of OHS practices. A comparative evaluation of present practices among three major types of Indian industry has been made. Further, the paper proposes an OHS instrument for Indian industry. The paper offers new directions for researchers to devise a comprehensive methodology that aims at reducing occupational health risks.

show abstract

Applying generalised feedforward neural networks to classifying industrial jobs in terms of risk of low back disorders

Cited by 21 publications

References 20 publications

Artificial intelligence approach to classify unipolar and bipolar depressive disorders

Artificial intelligence approach to classify unipolar and bipolar depressive disorders

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

Assessment of occupational health practices in Indian industries

Contact Info

Product

Resources

About